scholarly journals A Network Neuroscience Approach to Typical and Atypical Brain Development

2018 ◽  
Vol 3 (9) ◽  
pp. 754-766 ◽  
Author(s):  
Sarah E. Morgan ◽  
Simon R. White ◽  
Edward T. Bullmore ◽  
Petra E. Vértes
Keyword(s):  
Brain Development ◽  
Atypical Brain Development ◽  
Network Neuroscience

Biomarkers in Pediatric Magnetoencephalography

2020 ◽  
pp. 375-389
Author(s):  
Julia M. Stephen ◽  
Isabel Solis ◽  
John F. L. Pinner ◽  
Felicha T. Candelaria-Cook
Keyword(s):  
Brain Development ◽  
Spectral Power ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Population Characteristics ◽  
Fetal Alcohol ◽  
Replication Studies ◽  
Study Population ◽  
Age Range ◽  
Atypical Brain Development

The use of magnetoencephalography (MEG) to understand alterations in brain development in children has increased rapidly over the past two decades. Investigators have argued that MEG is an ideal neuroimaging tool for children because the technology is quiet and it provides high-density sensor systems. This participant-friendly technology has led to exploration of the use of MEG to identify biomarkers for atypical brain development to facilitate early diagnosis and intervention. Prior studies provide evidence that MEG is sensitive to a number of pediatric clinical disorders demonstrated through significant differences (e.g., latency, amplitude, spectral power) in children with autism spectrum disorder, children born prematurely, and children with fetal alcohol spectrum disorder, to name a few. At the same time, differences in age range, stimulus parameters, and study population characteristics contribute to variability in results across independent laboratories. While the current studies provide strong evidence for the sensitivity of MEG to identify brain abnormalities in children, replication studies are needed to validate biomarkers of atypical brain development to identify children at risk for atypical brain development. Additional studies are also needed to understand the dynamic changes in these brain markers across the age spectrum. Finally, future directions include gaining a broader understanding of typical and atypical brain development to identify neural targets for intervention.

scholarly journals DNA methylation and brain dysmaturation in preterm infants

2021 ◽  
Author(s):  
Emily N W Wheater ◽  
Paola Galdi ◽  
Daniel McCartney ◽  
Manuel Blesa ◽  
Gemma Sullivan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Functional Analysis ◽  
Preterm Birth ◽  
Brain Development ◽  
Gestational Age ◽  
Principal Component ◽  
Genome Wide ◽  
Gestational Age At Birth ◽  
Age At Birth ◽  
Atypical Brain Development

Preterm birth is associated with dysconnectivity of structural brain networks and is a leading cause of neurocognitive impairment in childhood. Variation in DNA methylation (DNAm) is associated with early exposure to extrauterine life but there has been little research exploring its relationship with brain development. Using genome-wide DNA methylation data from saliva of 258 neonates, we investigated the impact of gestational age on the methylome and performed functional analysis to identify enriched gene sets from probes that contributed to differentially methylated probes (DMPs) or regions (DMRs). We tested the hypothesis that variation in DNAm could underpin the association between preterm birth and atypical brain development by linking DMPs with measures of white matter connectivity derived from diffusion MRI metrics: peak width of skeletonised mean diffusivity (PSMD), fractional anisotropy (PSFA) and neurite density index (PSNDI). Gestational age at birth was associated with widespread differential methylation, with genome-wide significant associations observed for 8,870 CpG probes (p<3.6x10-8) and 1,767 differentially methylated regions. Functional analysis identified 14 enriched gene ontology terms pertaining to cell-cell contacts and cell-extracellular matrix contacts. Principal component analysis of probes with genome-wide significance revealed a first principal component (PC1) that explained 23.5% of variance in DNAm, and this was negatively associated with gestational age at birth. PC1 was associated with PSMD (β=0.349, p=8.37x10-10) and PSNDI (β=0.364, p=4.15x10-5), but not with PSFA (β=-0.035, p=0.510); these relationships mirrored the imaging metrics associations with gestational age at birth. Gestational age at birth has a profound and widely distributed effect on the neonatal saliva methylome. Enriched gene ontology terms related to cell-cell contacts reveal pathways that could mediate the effect of early life environmental exposures on development. Finally, associations between differential DNAm and image markers of white matter tract microstructure suggest that variation in DNAm may provide a link between preterm birth and the dysconnectivity of developing brain networks that characterises atypical brain development in preterm infants.

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